1. Field of the Disclosure
The present disclosure relates generally to systems and methods for decentralized analysis and control of power consumption in a power grid. More specifically, the present disclosure relates to a community energy management system that analyzes and controls power to consuming units within a community, including analyzing usage in the consuming units and controlling supply of backup power and demand response.
2. Background
In developed nations, during extreme summer days, demand for electricity peaks due to heavy usage of appliances, such as air conditioning. Utility companies, in order to handle the unexpected peak load, are forced to source the additional supply at a hefty premium. Ideally, utilities would like to control peak load in order to avoid sourcing power at an exorbitant price.
In emerging markets such as, for example, India, the supply of energy continuously lags behind demand. The current gap between peak demand and supply in India is approximately 12% for power and 1% for energy. As a result, there is typically less power than is desired.
In order to bring the demand below supply, utility companies simply shut off the supply of electricity to different areas of a city as per a scheduled—and many times an unscheduled—plan. This phenomenon forces rolling electricity shut down in emerging markets known as a blackout or load shedding. Rolling blackouts negatively affect the day-to-day lives of consumers. Brownouts, or degradation in power quality that may seriously affect appliance or device functionality, may often occur in addition to blackouts.
At community levels, a community of individual energy-consuming units (such as units, apartments, condos, industrial units and the like) typically provides backup generator power to the units during blackouts. The backup generator is usually a diesel-powered generator that is switched on either manually or automatically when electricity is cut (blackout) from the utility. When the generators come on, they provide all the electricity for the community during the blackout. The community (e.g., apartment complex) usually then charges each unit an equal percentage of the cost of maintaining the backup generators regardless of how much electricity or power was consumed during the time that the backup generators were operational. This may leave some unit owners frustrated, knowing some of their neighbors have appliances of far greater energy consumption.
Problems resulting from mismatch of power demand and supply are likely to worsen in countries over the next couple of decades. For example, given the 8.5% gross domestic product (GDP) growth rate of India, the demand for electricity in India is expected to more than double by 2020 to 400,000 MW and become more than four times current levels, or 950,000 MW, by 2030. The current production capacity is pegged at 150,000 MW. This implies issues with the widening supply-demand gap in electrical power in India.
While the government is working to increase production, the gap is widening due to increased consumption. To eliminate a power crisis, especially during the peak load periods, developed nations have proposed leveraging the sophisticated smart grid infrastructure that uses smart meters to monitor usage and demand of energy. This infrastructure employs a control center to send signals to smart appliances or smart meters to either provide price per unit increases at peak load time or to execute demand response in which certain loads are temporarily, but intelligently, shed in order to reduce load. The intelligence varies, but is usually to shed loads across multiple customers and in a way that is most convenient to the utility, for instance, in a way that is easy to track and account for in customer bills.
However, a smart grid solution may not be feasible in emerging markets for multiple reasons, a few of which include: (1) lack of smart grid infrastructure; (2) the prohibitive costs of deploying smart meters and smart appliances; and (3) the inability to scale to a level that would allow sufficient control to prevent rolling blackouts. Therefore, a need exists to better address the problems of excess demand.